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"AI Is Just Another Phase… Right?" 5 Myths About AI for NetOps

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I recently went through a detailed discussion around AI in network operations, specifically focused on the skepticism many engineers have seen before with previous technology waves. Some practical observations: • Most networks already generate large volumes of telemetry, logs, and tickets, but correlation remains manual • Troubleshooting often requires switching between multiple vendor tools and systems • AI becomes useful when it can read across telemetry, configurations, and tickets in one workflow • Vendor-specific AI tools are limited to their own ecosystems • A platform approach allows teams to build workflows tailored to their environment One misconception addressed was that AI is just another layer on top of existing tools. In practice, its value comes from connecting data across silos and providing answers that engineers can act on. Another key point was around build versus buy. Building everything internally provides control but creates long-term maintenance overhead. Usin...
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Evolving Packet Brokering for Modern Network Observability

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  I recently reviewed a technical overview describing how packet brokering platforms are evolving to support large-scale observability in modern data centers and service provider networks. The discussion focused on scalability, automated deployment, and operational efficiency. Some practical observations: • Network visibility platforms are expanding support for 400G switching hardware used in modern data center fabrics • GRE encapsulation enables mirrored traffic to move across Layer-3 networks while preserving packet metadata • Zero-Touch Provisioning simplifies onboarding for new or factory-reset switches • Packet brokers aggregate traffic from TAPs and SPAN ports and apply filtering and distribution policies • CLI improvements help operators manage large monitoring environments more efficiently One notable element was how GRE tunneling extends observability beyond Layer-2 boundaries. By encapsulating mirrored traffic into GRE tunnels, monitoring systems can receive full packet c...
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Evolving Packet Brokering for Modern Network Observability

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  I recently reviewed a technical overview describing how packet brokering platforms are evolving to support large-scale observability in modern data centers and service provider networks. The discussion focused on scalability, automated deployment, and operational efficiency. Some practical observations: • Network visibility platforms are expanding support for 400G switching hardware used in modern data center fabrics • GRE encapsulation enables mirrored traffic to move across Layer-3 networks while preserving packet metadata • Zero-Touch Provisioning simplifies onboarding for new or factory-reset switches • Packet brokers aggregate traffic from TAPs and SPAN ports and apply filtering and distribution policies • CLI improvements help operators manage large monitoring environments more efficiently One notable element was how GRE tunneling extends observability beyond Layer-2 boundaries. By encapsulating mirrored traffic into GRE tunnels, monitoring systems can receive full packet ...
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How Network Copilot Uses Agentic AI to Correlate FortiGate and Splunk

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I recently went through a detailed implementation explaining how Network Copilot correlates FortiGate firewall telemetry with operational data indexed in Splunk . What stood out was how grounded the discussion was in real operational workflows rather than theoretical AI claims. Some practical observations: • Network and security logs are typically distributed across multiple platforms • Troubleshooting often requires manually correlating events between systems • Network Copilot ingests metrics, events, logs, and traces from firewall environments • Operators can interact with the system using natural language questions • Explainable root cause analysis improves operational confidence One misconception addressed during the walkthrough was that AI in operations means replacing engineers. In reality, the system augments teams by surfacing insights faster and correlating information across environments. Another interesting point was how the platform learns from real firewall deployments...
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Scaling Deep Network Observability for 5G: Reflections from a Real Deployment

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  I recently went through a detailed implementation showing how deep network observability enhances 5G mobile user experience while reducing operational complexity. What stood out was how grounded the deployment was in real traffic and production-scale constraints. Some practical observations: • Fragmented systems increase cost and operational overhead • Frequent infrastructure upgrades create instability and repair expenses • Optimised filtering significantly reduces DPI hardware requirements • Real-time KPI computation improves SLA monitoring and service assurance • Standardised data export through streaming pipelines simplifies integration One misconception addressed during the walkthrough was that deeper observability automatically means more hardware. In reality, efficient filtering and intelligent packet processing reduce hardware footprint while improving performance. My biggest takeaway is that scalable observability in 5G environments requires architectural efficiency,...
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The Status Quo of Not Innovating in Network Observability: 5 Reasons Why Incumbent Solutions Are Holding You Back

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  Network observability is the lifeline of any modern digital infrastructure. Yet, for a space that’s so critical, it’s stuck in time. While the rest of the tech world is sprinting forward — driven by AI, cloud-native models, and vendor-neutral strategies — network observability is still clinging to a status quo that’s not just outdated, but  actively limiting. Let’s break it down. Here are five reasons why incumbent network observability solutions are failing us: 1. Still Hardware-First in a Software-First World In nearly every part of the infrastructure stack — from storage to compute to networking — we’ve seen a clear shift: software first, hardware second. Flexibility, scalability, and rapid innovation are made possible by decoupling software from rigid, proprietary hardware. Yet, most legacy network observability solutions still demand specialized boxes and appliances. Want to scale? Add more hardware. Want to adapt quickly? Sorry, you’re locked into a lifecycle that move...
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Introducing Aviz Service Nodes R2.0 — Empowering Enterprise, Data Center and Telco Deployments

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  In today’s ever-evolving network landscape, staying ahead means continuously innovating. Building on the trusted foundation of our previous releases — including the robust telco features of 1.3.1 — we are excited to introduce R2.0. This release is not just an update; it’s a reimagining of how enterprise and telco networks operate, bringing you enhanced performance, precision, and peace of mind What’s New in R2.0 In R2.0, we’ve focused on adding tangible value for our customers. Here’s a quick look at what you can expect: Enterprise & Telco Readiness:  Fully ASN-ready for seamless deployment across large-scale networks. Packet Deduplication for DC:  Optimizes network performance by eliminating redundant packet processing. Advanced DPI for IP Packets:  Leverages cutting-edge deep packet inspection for precise application identification. Enhanced Packet Capture:  Delivers robust support tailored for both telco and enterprise environments. The image below prov...
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